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Methods and Applications of Machine Vision

Module name (EN):
Name of module in study programme. It should be precise and clear.
Methods and Applications of Machine Vision
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Electrical Engineering - Renewable Energy and System Technology, Master, ASPO 01.10.2019
Module code: DFMEES-109
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P610-0134
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
4VU (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
5
Semester: 1
Mandatory course: yes
Language of instruction:
German
Assessment:
Exam

[updated 09.11.2022]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

DFMEES-109 (P610-0134) Electrical Engineering - Renewable Energy and System Technology, Master, ASPO 01.10.2019 , semester 1, mandatory course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 150 hours (equivalent to 5 ECTS credits).
There are therefore 105 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Marc Quirin, M.Sc.
Lecturer:
Marc Quirin, M.Sc.


[updated 23.09.2022]
Learning outcomes:
After successfully completing this module, students will be familiar with the practical methods and applications in industrial image processing. More specifically, students be familair with basic methods and algorithms in image processing. Students will be able to systematically plan and implement an image processing task, both in the design of the hardware and software. The programming exercises in this module will emphasize the theoretical principles taught in the “front-end” and “back-end” of the image processing chain.

[updated 09.11.2022]
Module content:
Module content:
1.        Introduction to the stages of machine vision
1.1.        Selection criteria for an image processing system
1.2.        Possible computations
1.3.        Image processing chain
2.        Technical basics for the “front-end” of the image processing chain
2.1.        Lighting
2.2.        Filters
2.3.        Lenses
2.4.        Basics of camera technology
2.5.        Transmitting image information to the computer
2.6.        Image artifacts
2.6.1.        Aliasing
2.6.2.        Image noise
3.        The “back-end” of the image processing chain
3.1.        Mathematical tools
3.2.        Camera model and camera calibration
3.3.        Color models
3.4.        Image representation
3.5.        Image preprocessing in spatial and frequency domain
3.6.        Morphological operators
3.7.        Segmentation
3.8.        Labeling
3.9.        Feature extraction
3.10        Classification
4.      Summary


[updated 09.11.2022]
Teaching methods/Media:
Blackboard, lecture notes, Matlab, LabVIEW, Python

[updated 09.11.2022]
Recommended or required reading:
Tönnies Klaus D.: Grundlagen der Bildverarbeitung, Addison-Wesley Verlag, 2005
Jähne B.: Digitale Bildverarbeitung. Springer, 5. Edition, 2002
Haberäcker Peter: Digitale Bildverarbeitung, Carl Hanser Verlag München Wien, 1987

[updated 09.11.2022]
 
[Sun Apr 28 12:55:41 CEST 2024, CKEY=dmuadms, BKEY=dfmees, CID=DFMEES-109, LANGUAGE=en, DATE=28.04.2024]